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+### **Module 1: Python Programming for Data Science**
+
+#### **Sub-module 1.1: Python Basics**
+- Data types and variables
+- Control structures (if-else, for loops, while loops)
+- Functions and lambda expressions
+- Exception handling
+
+#### **Sub-module 1.2: Advanced Python**
+- List comprehensions
+- Generators and iterators
+- Decorators
+- Context managers
+
+#### **Sub-module 1.3: Python Libraries**
+- NumPy (arrays, matrix operations)
+- pandas (dataframes, series, data manipulation)
+- Matplotlib (basic plotting, figures, and axes)
+- seaborn (statistical data visualization)
+
+---
+
+### **Module 2: Data Management and Manipulation**
+
+#### **Sub-module 2.1: Data Cleaning**
+- Handling missing data
+- Data type conversion
+- Normalizing and scaling
+
+#### **Sub-module 2.2: Data Exploration**
+- Descriptive statistics
+- Correlation analysis
+- Outlier detection
+
+#### **Sub-module 2.3: Data Wrangling**
+- Merging, joining, and concatenating data
+- Grouping and aggregation
+- Pivot tables and cross-tabulation
+
+---
+
+### **Module 3: Machine Learning**
+
+#### **Sub-module 3.1: Fundamentals of ML**
+- Supervised vs. unsupervised learning
+- Overfitting and underfitting
+- Train-test split
+- Cross-validation
+
+#### **Sub-module 3.2: Regression Algorithms**
+- Linear regression
+- Polynomial regression
+- Ridge and Lasso regression
+
+#### **Sub-module 3.3: Classification Algorithms**
+- Logistic regression
+- Decision Trees
+- Random Forests
+- Support Vector Machines (SVM)
+- k-Nearest Neighbors (k-NN)
+
+#### **Sub-module 3.4: Unsupervised Algorithms**
+- k-means clustering
+- Hierarchical clustering
+- Principal Component Analysis (PCA)
+
+#### **Sub-module 3.5: Ensemble Methods**
+- Bagging
+- Boosting
+- Stacking
+
+#### **Sub-module 3.6: Model Evaluation**
+- Confusion matrix
+- ROC-AUC
+- Precision-Recall
+- F1 Score
+
+---
+
+### **Module 4: Deep Learning**
+
+#### **Sub-module 4.1: Neural Networks Basics**
+- Perceptrons
+- Activation functions (ReLU, sigmoid, tanh)
+- Feedforward neural networks
+- Backpropagation and gradient descent
+
+#### **Sub-module 4.2: Advanced Neural Networks**
+- Convolutional Neural Networks (CNNs)
+- Recurrent Neural Networks (RNNs)
+- Long Short-Term Memory networks (LSTMs)
+- Autoencoders
+- Generative Adversarial Networks (GANs)
+
+#### **Sub-module 4.3: Frameworks and Tools**
+- TensorFlow
+- Keras
+- PyTorch
+
+#### **Sub-module 4.4: Model Optimization and Deployment**
+- Regularization techniques
+- Hyperparameter tuning (Grid search, Random search)
+- Model deployment (Flask, Docker)
+
+---
+
+### **Module 5: Special Topics**
+
+#### **Sub-module 5.1: Natural Language Processing (NLP)**
+- Text preprocessing (tokenization, stemming, lemmatization)
+- Word embeddings (Word2Vec, GloVe)
+- Sentiment analysis
+- Named Entity Recognition (NER)
+
+#### **Sub-module 5.2: Computer Vision**
+- Image processing basics
+- Object detection
+- Image classification
+
+#### **Sub-module 5.3: Time Series Analysis**
+- ARIMA models
+- Seasonal decomposition
+- Forecasting